A framework and tool for assessing Indigenous content in Canadian social work curricula
Bibliographic record
Abstract
Social Work faculties across Canada are mandated through policy and for historical, political, social, and moral reasons to include Indigenous content in their curriculum. While there is policy that mandates Indigenous content, there is no clear framework or tool to assist faculty members to examine how they can assess their curriculum to ensure it includes appropriate Indigenous content. This study has three objectives: 1) to articulate an Aboriginal Assessment Process for Social Work Curriculum (AAP-SWC) Framework. This self-assessment process will ensure that effective North American and community-based Aboriginal knowledge, skills, and values are incorporated in Social work curriculum. Emerging from the AAP-SWC Framework is the second objective of implementing the Self-Assessment Tool for Programs (SATP) based on an extensive literature review spanning the fields of Social Work, Education, and Indigenous studies. The SATP is a tool that is part of the AAP-SWC and aims to support the awareness of Indigenous peoples, issues, and the competencies needed to build capacities within Indigenous communities for self-determination and self-governance. The third objective of this study is the application of the SATP to the curricula of three Bachelor of Social Work programs in Canada. This assessment process foregrounds Indigenous knowledges and considers the unique and specific knowledges, skills, and values that social service providers need to work effectively with diverse Indigenous communities, groups, and individuals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.026 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.010 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".